Xafe offers a cognitive data privacy platform that secures sensitive data sharing and analysis using differential privacy. It manages privacy budgets and injects calibrated noise into query results, protecting individual data while maintaining statistical utility for compliance and insights.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises face challenges in securely sharing and analyzing sensitive data due to privacy regulations and the risk of individual re-identification. Traditional anonymization methods often degrade data utility, hindering effective insights and compliance efforts.
Solution
Xafe provides a cognitive data privacy platform that enables secure data sharing and analysis through the application of differential privacy techniques. The platform manages privacy budgets and injects calibrated noise into query results, ensuring that individual data points remain protected while preserving the statistical integrity of the overall dataset. This approach allows organizations to comply with privacy mandates and foster trust by balancing data utility with robust individual privacy protection. Xafe facilitates the creation of anonymized datasets and provides tools for real-time adjustment of privacy parameters to optimize the trade-off between privacy and data utility.
Target Audience
The platform targets enterprises, particularly in sectors like finance, that require robust data privacy solutions for secure data sharing and analysis.
Features
- Differential privacy implementation with explicit privacy budget management and tracking.
- Calibrated noise injection into query results to protect individual data entries.
- Generation of anonymized datasets through noise application to raw data.
- Tools for real-time adjustment of privacy parameters and visualization of utility trade-offs.
- Compliance-ready framework for sensitive data handling and sharing.
- Support for various use cases including fraud detection, customer data privacy, and risk modeling in financial services.